_base_ = ["./ld_r18_gflv1_r101_fpn_coco_1x.py"]
model = dict(
    pretrained="torchvision://resnet34",
    backbone=dict(
        type="ResNet",
        depth=34,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=True),
        norm_eval=True,
        style="pytorch",
    ),
    neck=dict(
        type="FPN",
        in_channels=[64, 128, 256, 512],
        out_channels=256,
        start_level=1,
        add_extra_convs="on_output",
        num_outs=5,
    ),
)
